Model based reasoning applied to electrical systems has matured over recent years resulting in deployment of commercial design analysis tools in the automotive industry [1, 41. These tools work at the component level on ...

Failure mode and effects analysis (FMEA) is typically performed by a team of engineers working together. In general, they will only consider single point failures in a system. Consideration of all possible combinations of ...

Recent work in Model Based Reasoning has resulted in the development of automated tools to perform Failure Mode Effects Analysis (FMEA) and Sneak Circuit Analysis (SCA). These tools work at the component level for individual ...

Almost all analyses of time complexity of evolutionary algorithms (EAs) have been conducted for (1 + 1) EAs only. Theoretical results on the average computation time of population-based EAs are few. However, the vast ...

This paper describes a project being undertaken at the University of Wales, Aberystwyth that captures students’ designs in an attempt to improve the pedagogy. To enhance their understanding of object oriented programming, ...

In spite of many applications of evolutionary algorithms in optimisation, theoretical results on the computation time and time complexity of evolutionary algorithms on different optimisation problems are relatively few. ...

Much research in model-based reasoning has concentrated on the use of a single, usually qualitative, level of modeling. This is less than ideal for many engineering applications, where the amount of knowledge available ...

This paper describes a project being undertaken at the University of Wales, Aberystwyth that aims to enhance the pedagogical process through the use of collaborative design. It describes a survey that highlights dissatisfaction ...

Probabilistic abduction extends conventional symbolic abductive reasoning with Bayesian inference methods. This allows for the uncertainty underlying implications to be expressed with probabilities as well as assumptions, ...

An approach to fuzzy rule induction inspired by the foraging behaviour of ants is presented. The implemented system - FRANTIC - is tested on a real classification problem against two other fuzzy rule induction algorithms, ...

Reasoning with fuzzy rule-based models has been widely applied to perform various real world classification tasks. The main advantage of this approach is that it supports inferences in the way people think and make judgements. ...

The use of fuzzy quantifiers in linguistic fuzzy models helps to build fuzzy systems that use linguistic terms in a more natural way. Although several fuzzy quantification techniques have been developed, the application ...

Semantics-preserving dimensionality reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern ...

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the ...

Due to the explosive growth of electronically stored information, automatic methods must be developed to aid users in maintaining and using this abundance of information effectively. In particular, the sheer volume of ...

This paper describes a project that aims to enhance student learning of Object Oriented Programming through the development of an interactive learning environment. Through a series of connected experiments we have sought ...

An overview of the application of evolutionary computation to fuzzy knowledge discovery is presented. This is set in one of two contexts: overcoming the knowledge acquisition bottleneck in the development of intelligent ...

A new approach to fuzzy rule induction from historical data is presented. The implemented system - FRANTIC - is a tested on a simple classification problem against a fuzzy tree induction algorithm, a genetic algorithm, and ...